KEYWORDS: Remote sensing, Computing systems, Optimization (mathematics), Control systems, Data modeling, Genetic algorithms, Stars, Network security, Monte Carlo methods, Visualization
End-users of Command and Control (C2) systems can struggle to effectively determine which Course of Action (CoA) should be chosen out of many complex options. A novel top-K recommender system has been developed to provide C2 end-users with comprehensive CoA recommendations through four critical measures named for explanation purposes only: Optimality, Diversity, Feedback, and Preference. The Optimality measure introduces a novel “Pareto-mesh” technique for quantitative CoA metrics, which offers robust and computationally efficient comparisons of Pareto-front percent coverages among ranking options. The Diversity measure also introduces a novel linchpin-based qualitative/quantitative technique that considers qualitative data’s Hamming distances, quantitative data’s Euclidean distances, and optimizes diversity of top-K CoA’s based a chosen top linchpin CoA using Maximin optimization. The Feedback measure is based on implicit user choices, peripheral simulation results, and real-world scenario results, with benchmarks of state-of-the-art collaborative filtering methods SVD, BiVAE, and NCF for this domain. The Preference measure is based on explicit user preference input of quantitative data, and ranks CoA’s based on a simple weighted sum against quantitative CoA metrics. Representative data related to C2 end-users, request attributes, and CoA’s is randomly generated to perform evaluations of each recommender system measure. Hyper-parameters used to calculate each measure are also globally tuned by means of a Genetic Algorithm multi-objective optimization technique, relying on a per-measure, inversion-minimizing optimization function. The designed recommender system’s hyper-parameter training time is on the order of hours and the system inference time is on the order of milliseconds.
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